code: def arg_parse():
      parser = argparse.ArgumentParser(
          description="Graph classification arguments."
      )
  
      parser.add_argument(
          "--device", type=str, help="CPU / GPU device."
      )
      parser.add_argument(
          "--epochs", type=int, help="Number of epochs to train."
      )
      parser.add_argument(
          "--dataset",
          type=str,
          help="Graph classification dataset. dd or enzymes.",
      )
      parser.add_argument(
          "--model", type=str, help="GCN, GAT, GraphSAGE or GIN."
      )
      parser.add_argument(
          "--batch_size",
          type=int,
          help="Batch size for graph classification.",
      )
      parser.add_argument(
          "--hidden_dim", type=int, help="Hidden dimension of GNN."
      )
      parser.add_argument(
          "--num_layers",
          type=int,
          help="Number of graph convolution layers.",
      )
      parser.add_argument(
          "--opt",
          type=str,
          help="Optimizer such as adam, sgd, rmsprop or adagrad.",
      )
      parser.add_argument(
          "--weight_decay", type=float, help="Weight decay."
      )
      parser.add_argument(
          "--dropout", type=float, help="The dropout ratio."
      )
      parser.add_argument("--lr", type=float, help="Learning rate.")
      parser.add_argument(
          "--skip",
          type=str,
          help="Skip connections for GCN, GAT or GraphSAGE if specified as last.",
      )
  
      parser.set_defaults(
          device="cuda:0",
          epochs=500,
          dataset="enzymes",
          model="GIN",
          batch_size=32,
          hidden_dim=20,
          num_layers=3,
          opt="adam",
          weight_decay=5e-4,
          dropout=0.2,
          lr=0.001,
          skip=None,
      )
      return parser.parse_args()
  
imports: import torch
  import argparse
  import skip_models
  import torch.nn
  import torch.nn.functional
  import torch_geometric.transforms
  import torch_geometric.nn
  import torch.optim
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import TUDataset
  from sklearn.metrics import *
  from torch.nn import Sequential, Linear, ReLU
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
label_desc: Use image node_id 1 for calling a global function with example usage: arg_parse() and returns: parser